We use linear/logistic regression, CrossNet [1]}, XGBoost [2]}, MLP, EBM [3]}, NAM [4]}, NodeGAM [5]}, NodeGA\(^{\text{2}}\) M, and HONAM as experimental methods.
All models, except XGBoost and EBM, is implemented using PyTorch.
For MLP, NAM, and HONAM, the number of hidden layers is set to 3, the numbers of hidden units are set to [32, 64, 32], and LeakyReLU is used as the activation function.
The number of units in CrossNet is set to 32.
All PyTorch models are trained for 1000 epochs with the learning rate 0.001, and the batch size is used as the number of data samples divided by 100 to reduce the training time.
The best validation model is selectd for the test.
For NodeGAM and NodeGA\(^{\text{2}}\) M, we use the default hyperparameters provided by [5]}.
We use the open-source version of XGBoost and EBM.
For XGBoost, the number of boosting rounds is set to 1000 to ensure convergence, and eta is set to 0.3.
For EBM, the number of boosting round is set to 20000 to ensure convergence, inner bags and outer bags are set to 8, and learning rate is set to 0.01
All models are trained with 5 random seeds, and we report mean scores and standard deviations.
